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NETWORK / 22 / NOVEMBER 2016 activities – and the various sources of that technology – are genuinely interesting. For me it demonstrates that third parties should be allowed access to the funding pot, reig- niting the enthusiasm of network operators who may have become comfortable with the funding and who lack the element of com- petitive drive that oen produces results. The Renewable Cornwall Project Wales and West Utilities has supported a study to create an energy supply and demand simulator to fully understand the impact of potential investment requirements in the gas network. The simulator enables investment needs to be identified, includ- ing generation costs and storage costs and then can estimate the resultant cost to the consumer for various options. What makes this project interesting is the input from consumers on what the future energy scenario in the UK should look like, a voice that, arguably, has not been not heard enough in the ongoing conversation. Workshops with local people in the Cor- nish peninsula revealed that if the area was to be powered entirely by renewable energy then customers would opt for energy stor- age to be used to meet the variable seasonal demand. The simulator started by charting hourly demand for heat, light and power over a 12-month period and then added in the available energy supply profiles for wind, solar and geothermal. The workshop participants then chose the generation mixes to be programmed into the simulator; principally 50% wind; 25% solar and 25% geothermal/other). Two Virtual World Asset Management The technology on display on Fugro Roames' stand would not have looked out of place at a computer games convention, for that is where the technology was developed. Its 3D visualisation soware is being used by SP Energy Networks to bring asset man- agement into the 21st century. SPEN's £3.5 million NIA-funded Virtual World Asset Management project is entering its second year. It is designed to quantify the benefits of using VWAM rather than con- ventional asset management programmes, with a key target being to reduce costs and improve network resilience through the development of a 3D virtual model. The model was built from data that was captured through a series of flights. SPEN says the model is accurate, and has confidence in the data, especially at high voltages. The model has revealed inaccura- cies in SPEN's existing network data. For example, 62.31% of its pole locations are up to 10 metres from their actual position. While SPEN has identified several ben- efits of using the model over conventional techniques, the process does have its limita- tions. Errors in the data have to be elimi- nated before it can be adopted as business as usual, and currently it is not an option for the 100% detection of LV service wire. The project has also looked at vegetation management. The technology can detect intrusions on the network, and is a fast and effective method of auditing circuits. But SPEN has concluded there are limited ben- efits of pursuing growth rates in the context of this project. The technology cannot completely replace manual inspections, but there is already clear evidence that this technology can be adopted in the networks industry to great benefit. N scenarios for meeting peaks were chosen; energy storage versus over-demand. The simulator revealed that if energy storage is to be used, there is a seasonal storage need of 500,000MWh. The capital costs of electrical storage in the UK have been estimated at £1m/MWh, suggesting an investment need of £500bn for such a scenario. The simulator also showed that 60% of the storage requirement came from the 25% solar mix, and use of that technol- ogy should therefore be minimised. In comparison, if over-generation were used as the solution, 95% of the required generation would be under-utilised. The cost of such over-generation would be a chal- lenge for consumers to fund (see below). The project revealed that many con- sumers' visions for the future may not be compatible with affordable bills. 600,000 Solar % in modelled electricity supply mix Total low carbon modelled HLP hourly (annual) supply Maximum hourly demand for HLP based on 2015 Modelled HLP hourly and annual supply profile matched with demand Electricity storage (MWh/a) Source: Wales and West Utilities 0 5 10 514,978 327,490 260,159 207,925 15 20 25 30 500,000 400,000 300,000 30,000 25,000 20,000 15,000 10,000 5,000 MWh 0 200,000 100,000 Annual electricity storage needed (MWh/a) – based on the percentage of solar- generated energy Scenario B: over-generation LCNI REVIEW